13 jul. 2018

Continuing with this post evaluating the LOCAL model developed in Resemble. This time I use the Monitor function (one of the Monitor functions I am developing).

I create different subsets from the validation sample set for the different categories, In this case is for one time of puppies, and I am evaluating the moisture content. We can see that there are two outliers that increase the SEP, so we have to see if we remove this samples for some reasons.

Let´s validate first with this type of puppy subset and check the statistics:

6 jul. 2018

Good results for the prediction of the validation samples (Xu, Yu) for protein. This is the XY plot where we can see in different colors the classes of the validation samples (different types of petfood). the SEP is 0.88 (without removing outliers) . Defining the data frame by classes will allow us to see the SEP for every class so we can check which class needs for more samples in the training database (Xr, Yr) or to check for other reasons.